Games are the perfect test bench to verify the performance of agent-based machine learning algorithms.
Examples of such approaches can be found in agents that can play ATARI games, Go, DOTA or Starcraft [1]}, [2]}, [3]}, [4]}.
Recently, [5]} presented the Fantasy Football AI (FFAI) framework.
FFAI is based on the Blood Bowl board game, a fully-observable, stochastic, turn-based and modern-style board game played on grid-based game board.
The framework provides environments to train reinforcement learning algorithms, as well as the implementation of scripted agents that can compete against each others.
Together with the FFAI framework, the authors organized three competitions, called Bot Bowl, where different agents compete against each other to define the winner.
